Vinay Prasad

B.Tech., Chemical Engg., I.I.T. Bombay, 1992

M. S., Chemical Engg., Kansas State University, 1994

Ph. D., Chemical Engg., Rensselaer Polytechnic Institute, 2001

Details of Research Interests:  

My research interests are in process design and control, with emphasis on applications to complex systems and systems with multiple scales of action. Specific applications of the research are in microelectronics systems, fuel cells and biological systems. I am interested in understanding and developing methods to design the structure and optimize the function of these systems and in developing analysis techniques tailored to these systems. The focus is on the optimization and control of large scale and nonlinear chemical processes, and application to industrially relevant processes.

Fundamental studies in process systems engineering

The research emphasizes strategies that allow us to inferentially control specific properties of chemical and biological systems. This requires detailed model descriptions, and goes hand in hand with the development of control and optimization strategies for distributed parameter systems. The focus is on the development of low order models, along with the development of criteria for constructing optimal representative data sets to be used in model reduction methods. Possible applications for these methods include nonlinear transient distributed parameter systems such as pressure swing adsorption systems, complex reaction networks, deposition and etching processes in microelectronics, and systems with particle size distributions such as crystallization and polymerization systems. The problems typically encountered include the control of spatial profiles, particle size distributions and material microstructure.

Another important research topic is the control of systems that are described by multiscale models. Most multiscale models developed are computationally intensive to solve. In order to use these multiscale models for process control and optimization, the development of compact models that capture the effect of parameters at the microscale on the macroscale behavior of the system is required. Our research emphasizes development of reduced order descriptions of multiscale models that combine the effects of microscale and macroscale behavior. These lower order models will be used to develop the control algorithm for the multiscale system. The most important application of this research is in the control of material microstructure.

Microelectronics systems

A direct application of research in multiscale modeling is the study of microelectronics systems. Process flows in microelectronic systems (especially in back end of the line processing) consist mainly of deposition, etching and planarization processes. A fundamental concern in this kind of processing is the structure and properties of materials at the grain scale. Very often, the material of interest is polycrystalline, with multiple grains and many grain interfaces. The ultimate properties of microelectronic devices depend on the grain structure of materials, operating conditions and process history. The objective is to conduct studies of the development of grain structure in deposition processes. This is because we typically seek to optimize and control properties that are governed by grain scale structure and properties, but our manipulated variables (flow, temperature, etc.) work at the reactor and equipment scale. Our research will focus on developing optimization and control strategies for grain scale properties by manipulating the operating conditions at the reactor scale, with multiscale models providing the link between the two quantities.

Fuel cell systems

Fuel cells are electrochemical devices that convert the chemical energy of a reaction directly into electrical energy, and are an important technology for a potentially wide variety of applications. These applications will be in a large number of industries worldwide; especially promising are the prospects for transportation power (fuel cell vehicles) and micropower. Ongoing research in fuel cells focuses both on hydrogen generation technology and fuel cell electrode and membrane design. From a theoretical viewpoint, we are interested in modeling transport in fuel cells, especially mass transport through membranes in polymer electrolytic membrane fuel cells; and in suggesting process improvements in design and control based on these modeling studies. Another focus is research on reforming for hydrogen production, with an emphasis on developing efficient control algorithms, optimizing techniques for separation of hydrogen produced in external reformers from contaminants such as carbon monoxide and carbon dioxide, and on optimizing the integration of the reforming section with the fuel cell stack.

Systems biology

Molecular biology has led to remarkable progress in our understanding of biological systems; the current focus is mainly on identification of genes and functions of their products, which are components of the system. A major challenge is to understand at the system level biological systems that are composed of components revealed by molecular biology. Our interests lie in establishing techniques to study the structure of biosystems (genes, metabolism, and signal transduction networks), identify the dynamics and control properties of these systems, and to develop methods to modify and optimize these systems. Another key aspect of research in this area is “reverse engineering” of biological systems, which can lead to the development of new control strategies and algorithms for chemical processes.

Representative Publications:

1. V. Prasad and B. W. Bequette, Nonlinear System Identification and Model Reduction using Artificial Neural Networks , Computers and Chem. Eng., 27, 1741-1754 (2003).

2. V. Prasad, M. O. Bloomfield, D. F. Richards, H. Liang, and T. S. Cale, Modeling and Simulation of Plasma Enhanced Processing for Integrated Circuit Fabrication , Vacuum, 65, 443-455 (2002).

3. M. K. Gobbert, V. Prasad, and T. S. Cale, Modeling and Simulation of Atomic Layer Deposition at the Feature Scale , J. Vacuum Science and Technology B, 20(3), 1031- 1043 (2002).

4. V. Prasad, M. Schley, L. P. Russo, and B. W. Bequette, "Multi-rate Nonlinear Model Predictive Control of a Styrene Polymerization Reactor", J. Process Control, 12(3), 353-372 (2002).

5. D. Nagrath, V. Prasad, and B. W. Bequette, A Model Predictive Formulation for Control of Open-loop Unstable Cascade Systems , Chemical Engineering Science, 57(3), 365-378 (2002).